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Record W4385739964 · doi:10.1075/ld.00153.dru

(Im)politeness mismatches in the multi-dialogic pragmatics of telecinematic satire

2023· article· en· W4385739964 on OpenAlexaff
Andrey S. Druzhinin, Tom Scholte, T.A. Fomina

Bibliographic record

VenueLanguage and Dialogue · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDialogicPolitenessLinguisticsPragmaticsPsychologyReflexivityConstruct (python library)Meaning (existential)TypologySociologyComputer sciencePedagogyPhilosophy

Abstract

fetched live from OpenAlex

Abstract The paper addresses the problem of (im)politeness in light of mismatches between what we/others say and what we/others mean in a multi-dialogic search for meaning where humans integrate all their competence-in-performance and co-construct situated relationships in a more or less sustainable way. We examine how these processes occur by analyzing (im)politeness mismatches in telecinematic satire using dialogic speech act typology and methods of the Mixed Game Model to describe and explain the communicative meta-meaning of (im)politeness. We demonstrate that in satire the dialogic semantics of (im)politeness is polyvalent, interactant-relative, temporally variable, scalar and self-reflexive because it is part of integrational language-in-use engagement with the world through which humans construct multiple relational domains and relationships in them.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.011
Scholarly communication0.0050.007
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.303
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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